Alexander Ratner

According to our database, Alexander Ratner authored at least 43 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Language Model Preference Evaluation with Multiple Weak Evaluators.

Zhengyu Hu, Jieyu Zhang, Zhihan Xiong, Alexander Ratner, Hui Xiong, Ranjay Krishna

CoRR, 2024

Found in the middle: Calibrating Positional Attention Bias Improves Long Context Utilization.

Cheng-Yu Hsieh, Yung-Sung Chuang, Chun-Liang Li, Zifeng Wang, Long T. Le, Abhishek Kumar, James R. Glass, Alexander Ratner, Chen-Yu Lee, Ranjay Krishna, Tomas Pfister

Proceedings of the Findings of the Association for Computational Linguistics, 2024

2023

Tool Documentation Enables Zero-Shot Tool-Usage with Large Language Models.

Cheng-Yu Hsieh, Si-An Chen, Chun-Liang Li, Yasuhisa Fujii, Alexander Ratner, Chen-Yu Lee, Ranjay Krishna, Tomas Pfister

CoRR, 2023

MaskSearch: Querying Image Masks at Scale.

Dong He, Jieyu Zhang, Maureen Daum, Alexander Ratner, Magdalena Balazinska

CoRR, 2023

On the Trade-off of Intra-/Inter-class Diversity for Supervised Pre-training.

Jieyu Zhang, Bohan Wang, Zhengyu Hu, Pang Wei Koh, Alexander J. Ratner

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias.

Yue Yu, Yuchen Zhuang, Jieyu Zhang, Yu Meng, Alexander J. Ratner, Ranjay Krishna, Jiaming Shen, Chao Zhang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Characterizing the Impacts of Semi-supervised Learning for Weak Supervision.

Jeffrey Li, Jieyu Zhang, Ludwig Schmidt, Alexander J. Ratner

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

DataComp: In search of the next generation of multimodal datasets.

Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, Jonathan Hayase, Georgios Smyrnis, Thao Nguyen, Ryan Marten, Mitchell Wortsman, Dhruba Ghosh, Jieyu Zhang, Eyal Orgad, Rahim Entezari, Giannis Daras, Sarah M. Pratt, Vivek Ramanujan, Yonatan Bitton, Kalyani Marathe, Stephen Mussmann, Richard Vencu, Mehdi Cherti, Ranjay Krishna, Pang Wei Koh, Olga Saukh, Alexander J. Ratner, Shuran Song, Hannaneh Hajishirzi, Ali Farhadi, Romain Beaumont, Sewoong Oh, Alex Dimakis, Jenia Jitsev, Yair Carmon, Vaishaal Shankar, Ludwig Schmidt

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Leveraging Instance Features for Label Aggregation in Programmatic Weak Supervision.

Jieyu Zhang, Linxin Song, Alex Ratner

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes.

Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alex Ratner, Ranjay Krishna, Chen-Yu Lee, Tomas Pfister

Proceedings of the Findings of the Association for Computational Linguistics: ACL 2023, 2023

2022

Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming.

Cheng-Yu Hsieh, Jieyu Zhang, Alexander J. Ratner

Proc. VLDB Endow., 2022

A Survey on Programmatic Weak Supervision.

Jieyu Zhang, Cheng-Yu Hsieh, Yue Yu, Chao Zhang, Alexander Ratner

CoRR, 2022

Understanding Programmatic Weak Supervision via Source-aware Influence Function.

Jieyu Zhang, Haonan Wang, Cheng-Yu Hsieh, Alexander J. Ratner

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Creating Training Sets via Weak Indirect Supervision.

Jieyu Zhang, Bohan Wang, Xiangchen Song, Yujing Wang, Yaming Yang, Jing Bai, Alexander Ratner

Proceedings of the Tenth International Conference on Learning Representations, 2022

Binary Classification with Positive Labeling Sources.

Jieyu Zhang, Yujing Wang, Yaming Yang, Yang Luo, Alexander Ratner

Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

2021

Proceedings of the First Workshop on Weakly Supervised Learning (WeaSuL).

Michael A. Hedderich, Benjamin Roth, Katharina Kann, Barbara Plank, Alex Ratner, Dietrich Klakow

CoRR, 2021

WRENCH: A Comprehensive Benchmark for Weak Supervision.

Jieyu Zhang, Yue Yu, Yinghao Li, Yujing Wang, Yaming Yang, Mao Yang, Alexander Ratner

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

2020

Cross-Modal Data Programming Enables Rapid Medical Machine Learning.

Jared A. Dunnmon, Alexander J. Ratner, Khaled Saab, Nishith Khandwala, Matthew Markert, Hersh Sagreiya, Roger E. Goldman, Christopher Lee-Messer, Matthew P. Lungren, Daniel L. Rubin, Christopher Ré

Patterns, 2020

Extracting chemical reactions from text using Snorkel.

Emily K. Mallory, Matthieu de Rochemonteix, Alexander Ratner, Ambika Acharya, Christopher Ré, Roselie A. Bright, Russ B. Altman

BMC Bioinform., 2020

2019

Accelerating machine learning with training data management.

Alexander Ratner

PhD thesis, 2019

Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices.

Vincent S. Chen, Sen Wu, Zhenzhen Weng, Alexander Ratner, Christopher Ré

CoRR, 2019

SysML: The New Frontier of Machine Learning Systems.

Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric S. Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros G. Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim M. Hazelwood, Furong Huang, Martin Jaggi, Kevin G. Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Gordon Murray, Dimitris S. Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Randall Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric P. Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar

CoRR, 2019

Osprey: Weak Supervision of Imbalanced Extraction Problems without Code.

Eran Bringer, Abraham Israeli, Yoav Shoham, Alexander Ratner, Christopher Ré

Proceedings of the 3rd International Workshop on Data Management for End-to-End Machine Learning, 2019

Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale.

Stephen H. Bach, Daniel Rodriguez, Yintao Liu, Chong Luo, Haidong Shao, Cassandra Xia, Souvik Sen, Alexander Ratner, Braden Hancock, Houman Alborzi, Rahul Kuchhal, Christopher Ré, Rob Malkin

Proceedings of the 2019 International Conference on Management of Data, 2019

Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices.

Vincent S. Chen, Sen Wu, Alexander J. Ratner, Jen Weng, Christopher Ré

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Doubly Weak Supervision of Deep Learning Models for Head CT.

Khaled Saab, Jared Dunnmon, Roger E. Goldman, Alexander Ratner, Hersh Sagreiya, Christopher Ré, Daniel L. Rubin

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2019, 2019

Learning Dependency Structures for Weak Supervision Models.

Paroma Varma, Frederic Sala, Ann He, Alexander Ratner, Christopher Ré

Proceedings of the 36th International Conference on Machine Learning, 2019

A Kernel Theory of Modern Data Augmentation.

Tri Dao, Albert Gu, Alexander Ratner, Virginia Smith, Chris De Sa, Christopher Ré

Proceedings of the 36th International Conference on Machine Learning, 2019

The Role of Massively Multi-Task and Weak Supervision in Software 2.0.

Alexander J. Ratner, Braden Hancock, Christopher Ré

Proceedings of the 9th Biennial Conference on Innovative Data Systems Research, 2019

Training Complex Models with Multi-Task Weak Supervision.

Alexander Ratner, Braden Hancock, Jared Dunnmon, Frederic Sala, Shreyash Pandey, Christopher Ré

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Knowledge Base Construction in the Machine-learning Era.

Alexander Ratner, Christopher Ré

ACM Queue, 2018

Research for practice: knowledge base construction in the machine-learning era.

Alexander Ratner, Christopher Ré, Peter Bailis

Commun. ACM, 2018

Snorkel MeTaL: Weak Supervision for Multi-Task Learning.

Alexander Ratner, Braden Hancock, Jared Dunnmon, Roger E. Goldman, Christopher Ré

Proceedings of the Second Workshop on Data Management for End-To-End Machine Learning, 2018

2017

Incremental knowledge base construction using DeepDive.

Christopher De Sa, Alexander Ratner, Christopher Ré, Jaeho Shin, Feiran Wang, Sen Wu, Ce Zhang

VLDB J., 2017

Snorkel: Rapid Training Data Creation with Weak Supervision.

Alexander Ratner, Stephen H. Bach, Henry R. Ehrenberg, Jason Alan Fries, Sen Wu, Christopher Ré

Proc. VLDB Endow., 2017

SwellShark: A Generative Model for Biomedical Named Entity Recognition without Labeled Data.

Jason A. Fries, Sen Wu, Alexander Ratner, Christopher Ré

CoRR, 2017

Snorkel: Fast Training Set Generation for Information Extraction.

Alexander J. Ratner, Stephen H. Bach, Henry R. Ehrenberg, Christopher Ré

Proceedings of the 2017 ACM International Conference on Management of Data, 2017

Learning to Compose Domain-Specific Transformations for Data Augmentation.

Alexander J. Ratner, Henry R. Ehrenberg, Zeshan Hussain, Jared Dunnmon, Christopher Ré

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Learning the Structure of Generative Models without Labeled Data.

Stephen H. Bach, Bryan Dawei He, Alexander Ratner, Christopher Ré

Proceedings of the 34th International Conference on Machine Learning, 2017

Snorkel: A System for Lightweight Extraction.

Alexander Ratner, Stephen H. Bach, Henry R. Ehrenberg, Jason Alan Fries, Sen Wu, Christopher Ré

Proceedings of the 8th Biennial Conference on Innovative Data Systems Research, 2017

2016

DeepDive: Declarative Knowledge Base Construction.

Christopher De Sa, Alexander Ratner, Christopher Ré, Jaeho Shin, Feiran Wang, Sen Wu, Ce Zhang

SIGMOD Rec., 2016

Data programming with DDLite: putting humans in a different part of the loop.

Henry R. Ehrenberg, Jaeho Shin, Alexander J. Ratner, Jason A. Fries, Christopher Ré

Proceedings of the Workshop on Human-In-the-Loop Data Analytics, 2016

Data Programming: Creating Large Training Sets, Quickly.

Alexander J. Ratner, Christopher De Sa, Sen Wu, Daniel Selsam, Christopher Ré

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016